The use of open system pyrolysis, as used in the commercial Rock Eval™ analysis, and total organic carbon measurement employed by the Rock Eval™ and LECO™ analysis systems are methods that have long been used in the assessment of petroleum source rocks. See, e.g., Peters, K. E., 1986, Guidelines for Evaluating Petroleum Source Rock Using Programmed Pyrolysis, Bulletin of the American Association of Petroleum Geologists, v. 70, p. 318-329; U.S. Pat. No. 5,811,308, Expitalie et al., Method for Determining Petroleum Characteristics of Geologic Sediments Sep. 22, 1998; Langford, F. F. and M.-M. Blank-Valleron, 1990, Interpreting Rock-Eval Pyrolysis Data Using Graphs of Pyrolizable Hydrocarbons vs. Total Organic Carbon, Bulletin of the American Association of Petroleum Geologists, v. 74, p. 799-804; Lafargue, E., J. Expitalié, F. Marquis, and D. Pillot, 2000, Rock-Eval 6 Applications in Hydrocarbon Exploration, Production, and in Soil Contamination Studies: Revue de L'institut Francais du Parole, Vol. 53, No, 4, p. 421-437. These methods, however, rely on bulk measurements of all organic matter present in a sample and can only provide information from empirically-derived cutoff values and parameters. Methods and systems described in U.S. Pat. No. 7,363,206 and WO 2008/100614 (PCT/US2008/002102) demonstrate that pyrolysis data can be used to characterize the relative amounts of organic matter and contaminants in geological samples.
The Pyrolytic Oil-Productivity Index (POPI) technology originally disclosed in U.S. Pat. No. 5,866,814 has been used successfully in exploration and development wells to quickly and accurately assess the reservoir rock for tar mats and other types of organic matter as the drilling progresses through these layers in the reservoir rock. The POPI method has been incorporated in the commercially available software program identified as GC-ROX™ which is an acronym for “Geochemical Residual Oil eXpert-modeling”. The GC-ROX™ program can be used to optimize oil field development by tar mat identification and quantification, and can be used to validate, organize and store field-data. Earlier observations from various field data had suggested a cut-off of productivity exists at about 3% tar. However, relying on the more precise information produced by the GC-ROX software, a target of 1% tar has been used commercially for injectivity purposes. Interest has been shown in linking the POPI method and information to viscosity. It is thought that proxies could be developed, but the main application of the technique is identification and quantification of tar.
The process of petroleum source rock maturation and hydrocarbon generation is well understood, with the effects of burial and temperature causing kerogen to break down into bitumen, free oil that is expelled and migrates, and eventually into gas. Methods using compositional modeling are disclosed in U.S. Pat. No. 7,363,206 which permit the user to identify the relative proportions by percent of a plurality of end member components in a sample of reservoir rock. This methodology represented a significant advance in the art, since prior methods only provided bulk values or parameters.
The compositional modeling method for assessing residual hydrocarbon staining that is taught in U.S. Pat. No. 7,363,206 at column 6, line 56 to column 9, line 48, is preferred for use in the present invention. As used herein and specifically in the claims, the term “compositional modeling” shall mean the method disclosed in U.S. Pat. No. 7,363,206 and as described below. The disclosure of U.S. Pat. No. 7,363,206 is incorporated herein by reference.
These factors are: (1) the amount of total hydrocarbon yield, and (2) the similarity of the hydrocarbon staining to the produced oils. Pyrolysis instruments are useful for quantifying the amount of hydrocarbon staining and the POPI method assesses the similarity to produced oils by subdividing the hydrocarbons into the Light Volatile (LV), Thermally Distillable (TD), and Thermally Crackable (TC) components (FIG. 1). However, it has been discovered that visual inspection of pyrograms also can be useful in assessing the type of hydrocarbons present because oil, tar, pyrobitumen, and other typical organic matter each also have very characteristic appearances.
FIGS. 2a through 2d are examples of pyrograms for samples with a nearly uniform composition of specific hydrocarbon or pyrolytically identifiable type of organic matter end-member components. These plots show the hydrocarbon yield on the y-axis for each data step that is recorded on the x-axis.
The number of data steps for a particular analysis can vary based on the type of pyrolyis instrument used. Two such commercially available instruments are Vinci's ROCK-EVAL™ and Humble's SOURCE ROCK ANALYZER™. The data that is obtained from the instrument and converted into a digital file can also vary. In the case description and examples that follow, the SOURCE ROCK ANALYZER™ was used and the data were output into digital form using a MICROSOFT EXCEL™ CSV file that recorded the yield and temperature over 611 data steps. The first 111 steps record the isothermal hold at 195 degrees C. for 3 minutes and the next 500 steps record the programmed temperature run from 195 degrees C. to 630 degrees C. In general, the temperature associated with any specific step is the same from run to run, so that the step number can be associated with the temperature of the oven during the run.
Compositional modeling for a sample includes entering in an appropriately programmed computer the yield at each individual data step as a value that is made up of the aggregate yield of the various end-member components. In this method, a specific and consistent temperature is associated with each step. The difference between the modeled yield calculated in accordance with the algorithm from the theoretical end-member component or components and the actual yield provides the basis for assessing whether a particular solution accurately reflects the actual composition in the reservoir rock sample.
Each such solution that is assessed must sum the difference between the calculated yield and the actual yield over all the data steps for the sample. Any of a number of statistical methods can be used in quantifying the overall error for any proposed solution. The modeling relies on iteratively varying the concentration of the various components until the aggregate error is minimized and the curves appear very similar. In one preferred embodiment, the software utilizes the iterative process of proposing different compositions, calculating a hypothetical curve based on the yield at each data step, assessing the error for each particular solution, and then minimizing this aggregate error. These method steps can advantageously be completed by the use of macros and the Solver add-on application that is a standard component of the Microsoft Excel™ program, and its use greatly automates and expedites the process. There are other software packages that can also be utilized to facilitate the methods used to model hydrocarbon composition that are commercially available and include Corel Quattro Pro, Lotus 1-2-3, Corel Paradox, Lotus Approach, Microsoft Access and Microsoft Visual FoxPro.
Table 1, shown in FIG. 6, is the output file from a Humble SOURCE ROCK ANALYZER™ that has been converted to CSV format. The output data file records the same information in the same location for each sample tested and facilitates its extraction by spreadsheet data analysis programs. The header information at the very top of the report records the calculated parameters from the instrument and the run parameters. Starting at row 22, the instrument records the curve signal in the first three columns of the file. The first column contains the data step number, the second column records the signal from the flame ionization detector (FID) in milliVolts (mV), and the third column records the temperature of the oven associated with the data step.
In order to convert the output of the instrument into hydrocarbon yield using the instrument software, a known standard compound or composition from the reservoir region is analyzed. With the data from the standard, the instrument can calculate the conversion factor (CF) to relate millivolts from the FID to hydrocarbon yield in units of milligrams per gram of rock (g Rock). From the data file, these conversion factors are calculated for each sample by summing the total signal in column two and then dividing this signal by the total hydrocarbon yield of the sample in accordance with the following mathematical expression:CFFID=[ΣSignalstep 1-611 (mV)]/[(LV+TD+TC) (mgHC/g Rock)]  (1)
The signal that is taken for any particular data step is then be converted into mgHC/gRock by simply dividing the signal by CF:Yieldstep X (mgHC/g Rock)=[SignalSTEP X (mV)]/[CFFID (mV/mgHC/g ROCK)]  (2)
In a preferred embodiment, all instrument output is converted into yields for the purpose of making the relevant calculations to combine end-members (EM) and to compute the results based on modeled solutions. This is done because the actual yield that is given for each end-member sample and for each sample that is being modeled will be unique. In order to model the relative composition of end-members that make up a particular sample, the data for each end-member is normalized so that the total hydrocarbon yields of each recalculated end-member is the same as the actual sample. Therefore, the quantity of an end-member component that would be present for a pure end-member having the same yield as the sample can be expressed as follows:Yield=[YieldEMSTEPX]*([Total Yield(THC)Sample]/[Total Yield End-Member])  (3)In the above equation (3) and those that follow the “*” notation is used to indicate multiplication.
Equation (3) is used to calculate the aggregate yield that would be found for a hypothetical sample that contained various percentages of end-members that are needed to describe the sample behavior. Thus, the calculated yield for a proposed hydrocarbon composition at any given data step is the sum of the percentages of each end-member (% EM1 to 5) divided by 100 and multiplied by the yield of the end-member at step x (YieldEM1 to 5,X) times the ratio of the total yield of the sample divided by the total yield of the end-member (THCsample/THCEM1 to 5). This step can be expressed as follows:
                              Calculated          ⁢                                          ⁢                      Yield                                                            EM                  ⁢                                                                          ⁢                  1                                -                5                            ,              X                                      =                                            (                              %                ⁢                                                                  ⁢                                  EM                  1                                ⁢                                  /                                ⁢                100                            )                        *                          Yield                                                EM                  ⁢                                                                          ⁢                  1                                ,                X                                      *                          (                                                THC                  sample                                ⁢                                  /                                ⁢                                  THC                                      EM                    ⁢                                                                                  ⁢                    1                                                              )                                +                                    (                              %                ⁢                                                                  ⁢                                  EM                  2                                ⁢                                  /                                ⁢                100                            )                        *                          Yield                                                EM                  ⁢                                                                          ⁢                  2                                ,                X                                      *                          (                                                THC                  sample                                ⁢                                  /                                ⁢                                  THC                                      EM                    ⁢                                                                                  ⁢                    2                                                              )                                +                                    (                              %                ⁢                                                                  ⁢                                  M                  3                                ⁢                                  /                                ⁢                100                            )                        *                          Yield                                                EM                  ⁢                                                                          ⁢                  3                                ,                X                                      *                          (                                                THC                  sample                                ⁢                                  /                                ⁢                                  THC                                      EM                    ⁢                                                                                  ⁢                    3                                                              )                                +                                    (                              %                ⁢                                                                  ⁢                                  EM                  4                                ⁢                                  /                                ⁢                100                            )                        *                          Yield                                                EM                  ⁢                                                                          ⁢                  4                                ,                X                                      *                          (                                                THC                  sample                                ⁢                                  /                                ⁢                                  THC                                      EM                    ⁢                                                                                  ⁢                    4                                                              )                                +                                    (                              %                ⁢                                                                  ⁢                                                      EM                    5                                    /                  100                                            )                        *                          Yield                                                EM                  ⁢                                                                          ⁢                  5                                ,                X                                      *                          (                                                THC                  sample                                ⁢                                  /                                ⁢                                  THC                                      EM                    ⁢                                                                                  ⁢                    5                                                              )                                                          (        4        )            
The error between a particular modeled solution for Step X and the actual analytical result for Step X can be obtained by simple difference. However, since some of these values will be positive and some negative, the treatment of errors for all values calculated, e.g., Steps 1-611, is easier to accomplish through the application of a root mean squares (RMS) calculation. Other statistical treatments can be used to also achieve the same results if they employ the difference between each modeled yield and actual yield as an absolute value.
In one preferred method, the RMS average difference is calculated in terms of a percentage that relates to the total response of the sample and can be expressed as follows:%RMSCALC vs ACTUAL=100*((AVERAGESTEP, 1-611(YieldCALC−YieldACTUAL)2)1/2/(AVERAGESTEP, 1-611(YieldACTUAL))  (5)
The modeling process comprises the steps of varying the percentage of the end-members that are present in the system in which (EM1-5 are preferably used) until the calculated curve matches the actual curve and the % RMS error is minimized. Due to the fact that so many calculations must be made to assess any solution, the use of a spreadsheet program to perform these calculations and automatically plot the result that is achieved greatly simplifies the process. Moreover, a software application such as Solver that is present as an add-on in Microsoft Excel™, can greatly expedite the data processing capability of iteratively solving problems with multiple variables that seek to converge on a desired solution which in this case is minimizing error.
FIG. 3 shows the graphic interface that was utilized for a five end-member component system at well-site Z. The graphic illustration includes curves for the current sample, the calculated solution based on the percentage of the components, the oil end-member, the tar end-member, the shale end-member (typical of dispersed kerogen found in shaley lithologies), the coal end-member, and the drilling mud end-member (contamination). The parameter listed on the top line as DEVRM is the RMS deviation as a percent of total yield and is the value that is minimized in obtaining a reasonable solution for a given sample. When all samples are analyzed for a particular well, the results can be plotted as in FIG. 4 to reveal how the composition varies throughout the sampled section. The numerical references 1-4 on the graph are to the legends at the top. Plots such as these are very useful in identifying important trends, such as increasing tar, or in identifying individual coal or tar units that may have important implications in reservoir performance. An alternative method of presenting the data as is shown in FIG. 5, is to plot the relative contribution of each EM component by depth with each curve being adjusted for changes in yield in the samples. This type of plot is particularly useful for identifying true tar mats that typically have an associated dramatic increase in hydrocarbon yield as opposed to a change in composition that appears to be tar, but is present in relatively low concentrations and not likely to affect reservoir performance.
As used herein, it will be understood from the description that “compositional modeling” includes the following steps:                a. identifying the end-member components known to be present in reservoir rock in the oil field;        b. preparing individual pyrograms consisting of a pcd for each of the components identified in step (a);        c. storing the pcd for each component in a digital data file;        d. conducting a pyrolytic analysis of a sample from the oil field of reservoir rock that contains one or more hydrocarbon and organic matter components of the type identified in step (a) to obtain pcd for the sample;        e. comparing the pcd for the sample with the pcd obtained in step (b) for each of the components, measuring and recording the difference between the sample pcd and the pcd for each of the components;        f. applying a statistical analysis to minimize the aggregate differences between the pcd for the sample and a combination of pcd selected from the components;        g. recording for retention and display for analysis the resulting pcd that constitutes the minimum aggregate error over the temperature range of the pyrolytic analysis; and        h. analyzing the displayed data to identify the respective components.        
As used herein, “unconventional oil” and “unconventional oil sources” include oil shales; organic-rich fine-grained carbonates, low-porosity low-permeability sandstones/siltstones/carbonates that are adjacent to hydrocarbon source units; oil sands, and heavy crude oils. Unconventional oil reservoirs are often source rocks that are being exploited to produce hydrocarbons that were unable to migrate as in conventional hydrocarbon reservoirs. One problem faced by geoscientists is how to classify the organic matter and hydrocarbon components at specific depths and/or intervals in the well, since it is recognized that such information, if available, would be extremely useful in assessing the reservoir during exploration and development drilling.
Knowledge of the quantity and types of organic matter and hydrocarbons present in unconventional reservoirs is critical to well performance evaluation and geochemical methods are used to provide this information. Traditional petrophysical analysis focuses principally on the rock matrix; however, the storage and flow capacity of unconventional reservoirs is highly dependent on the organic porosity resulting from the transformation of kerogen. Although total organic carbon (TOC) values are important to reservoir evaluation and assessment, traditional TOC measurement is not available at the well site in real-time in order to meaningfully assist in optimizing drilling operations.
The prior art methods require either time-consuming sample processing, such as the demineralization needed for the LECO™ analyzer, e.g., the LECO (EC 12) carbon analyzer; and preparation of samples using the TOC measurement or they require long analytical times by more complex instrumentation that is not suitable for use at well drilling sites where the rock samples are recovered from the drilling fluid. In addition, the prior art methods are bulk analytical methods which do not differentiate between the materials present in mixtures.
A new method that can be deployed at the well site to provide information that can be used to guide the drilling operation for exploratory and development wells is needed.
Definitions
As used herein, it is to be understood that the following terms and designations have the meanings indicated:
Bitumen—generally refers to the organic material that is extractable through the use of organic solvents and encompasses those obtained through the use of strong organic solvents such as methylene chloride, chloroform, and toluene.
End Member (EM)—A consistent type of organic matter or hydrocarbon that can be distinguished by pyrolytic analysis. End members include oil, soluble tar, pyrobitumen (insoluble tar), kerogen, coal, drilling mud and other contaminants. Specific end members are associated with specific fields and reservoirs. A reference to “local” end members means end members that have been determined to be present in the field and/or nearby wells based on analysis and examination of core samples, well logs, and drilling rock samples, also referred to below as “comparative samples”.
EMx Weight OM—the weight attributable to the organic matter (in milligrams per gram of rock) of one component end member (x) in a sample.
EMx Weight NSOs OM—the weight attributable to the elemental nitrogen, sulfur, and oxygen content (in milligrams per gram of rock) of one component end member (x) in a sample.
EMx Weight H—the weight attributable to the elemental hydrogen content (in milligrams per gram of rock) of one component end member (x) in a sample.
FID—Flame Ionization Detector
Free Oil—hydrocarbons that have been generated through the maturation of kerogen that are similar to producible oil and may be expelled from a source rock given a sufficient saturation level.
HC—Abbreviation for hydrocarbons, THC is used for Total Hydrocarbons.
H/COM—the ratio of hydrogen to carbon in the organic matter of an end member component.
Inert Carbon—Organic Carbon, that can be oxidized to CO2 and CO by application of heat as in traditional source rock analysis in the oxidation cycle or by LECO carbon analyzer, but that cannot be liberated by pyrolysis in an inert atmosphere either due to character of the organic matter or the adsorptive properties of the mineral matrix.
Kerogen—the product of biochemical degradation, polycondensation, insolubilization of biologically-derived material due to burial, time and temperature.
LV—Abbreviation for light volatile components. As used herein, LV refers specifically to the weight in milligrams of HC released per gram of rock at the initial static temperature condition of 180° C. (when the crucible containing the rock sample is inserted into the pyrolytic chamber) prior to the temperature-programmed pyrolysis of the sample.
Non-Pyrolizable Hydrocarbons (HCNon-py)—compounds composed primarily of carbon and hydrogen that cannot be liberated through evaporation or decomposition of organic material during pyrolysis and require combustion to analyze the different elemental components.
NOSx%—the weight percent of Nitrogen, Oxygen, and Sulfur in the organic matter of an end member component.
Organic Matter (OM)—generally biologically produced materials that are composed primarily of carbon and hydrogen, such as shale- and coal-like materials.
OM/HCpy—the ratio of organic matter to pyrolizable hydrocarbons in an end member component.
POPI—Abbreviation for the Pyrolytic Oil-Productivity Index. The POPI is calculated from the pyrolytic data by the following equation: POPI=ln(LV+TD+TC)×(TD/TC), where ln is the logarithmic value and TD and TC are as defined below.
Pyrolizable Hydrocarbons (HCpy)—compounds composed primarily of carbon and hydrogen that can be liberated through evaporation or decomposition of organic material in response to the application of heat in an inert atmosphere (i.e., not containing oxygen).
Pyrolytic Characterization Data (pcd)—Data values measured at a predetermined number of data points, each data point corresponding to a prescribed temperature.
Rock-Eval™ S1 Yield—weight in milligrams of HC released per gram of rock at the initial static temperature condition of 300° C. of a standard Rock-Eval™ pyrolysis analysis as described in Peters, K. E., 1986, Guidelines for Evaluating Petroleum Source Rock Using Programmed Pyrolysis, Bulletin of the American Association of Petroleum Geologists, v. 70, p. 318-329.
Rock-Eval™ S2 Yield—weight in milligrams of HC released per gram of rock during the programmed pyrolysis portion of a standard Rock-Eval analysis, where the temperature is raised from 300° C. to 550-600° C. at a rate of 25° C./minute, as described in Peters, K. E., 1986, Guidelines for Evaluating Petroleum Source Rock Using Programmed Pyrolysis, Bulletin of the American Association of Petroleum Geologists, v. 70, p. 318-329.
Source Rock—A sedimentary rock, deposited in a low-energy environment, generally of fine-grained nature (i.e., composed of silt and clay-sized particles), typically fissile, commonly composed of silica, clay, and carbonate minerals, and with sufficient organic matter content and quality to generate and expel hydrocarbons (i.e., petroleum).
TD—Abbreviation for “thermally distillable” components that, as used herein, refers specifically to the weight in milligrams of HC released per gram of rock at a temperature between 180° C. (195° C. on a Humble SR Analyzer) and Tmin(° C.).
TC—Abbreviation for “thermally crackable” components that as used herein, refers specifically to the weight in milligrams of HC released per gram of rock at a temperature between Tmin(° C.) and 600° C. (630° C. on a Humble SR Analyzer).
THC—Abbreviation for total hydrocarbons.
Tmin—The temperature at which the hydrocarbon yield, as measured by a flame ionization detector (FID) during pyrolysis employing the POPI method, reaches a minima between the peaks representing the thermally distilled and thermally cracked hydrocarbon peaks, generally occurring between 380° C. and 420° C.
Total Hydrocarbon Index (THI)—Represents the total HC released, including during the initial heating and programmed pyrolysis from 195° C. and 630° C., relative to Total Organic Carbon in a sample. The equation for calculating THI is: THI=[(LV+TD+TC)/TOC]×100.
THIOM—the Total Hydrocarbon Index of organic matter in an end member component.
Total Organic Carbon (TOC)—The TOC is the weight percent of organic carbon found in a rock sample.
TOCEMx [%]—The total organic carbon in weight percent that is attributable to a specific end member. This is found by taking total weight of the organic matter represented by the end member (EMx Weight OM), subtracting the weight of the end member attributable to elemental Nitrogen, Sulfur, and Oxygen (EMx Weight NSOs), subtracting the weight of the end member attributable to elemental hydrogen (EMx Weight H), then converting it to a decimal by dividing by 1000 mg/g Rock, and finally multiplying by 100 to put the number into percent form.
TOCinert—The portion of total organic carbon in weight percent that cannot be decomposed and detected by pyrolysis, but requires measurement of carbon dioxide and monoxide produced during an oxidation step at high temperature.
TOCRCN—The reconstructed total organic carbon, which is not directly measured, but rather by summing the TOC for each end member and the inert carbon present in the sample (also known as, TOCCoMod) as follows: TOCRCN=TOCEM1+TOCEM2 . . . . TOCEMx+TOCinert.
Wt % HOM—the weight percent of Hydrogen in the organic matter as determined by CHNOS elemental analysis.
YieldEMx—the hydrocarbon yield (in milligrams of hydrocarbon per gram of rock) that is attributable to each end member from applying compositional modeling as described in U.S. Pat. No. 7,363,206.